Artificial intelligence-based property data matching system

ABSTRACT

A data linking system is described herein that links data records corresponding to a particular real estate property even if there are inconsistencies in the data records, the physical presence of the real estate property has changed over time, and/or the data records use different terminology. In some cases the data records are matched using a trained machine learning model. The data linking system can optionally generate a visualization of the data record linkage via interactive user interfaces. By linking data records despite the issues described above, the data linking system reduces the number of navigational steps a user performs to obtain data associated with a property and/or reduces data processing times. The disclosed system may be used to generate and maintain a comprehensive database of substantially all properties within a jurisdiction, in which a unique identifier is assigned to each property.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application No. 62/654,183, entitled “SYSTEM FOR ASSIGNINGUNIQUE IDENTIFIERS TO PARCELS OF REAL PROPERTY” and filed on Apr. 6,2018, which is hereby incorporated by reference herein in its entirety.

BACKGROUND

Data corresponding to real estate property may be stored in manydifferent data stores accessible via a network. For example, tax dataassociated with a property may be stored in one data store, whereaslisting data associated with the property may be stored in another datastore. Each data store may include records that identify values fordifferent attributes of a property. For example, one record may identifythe address, property owner, deed record date, and/or the like for aproperty. Values for some property attributes may be present in recordsin multiple data stores. However, values for other property attributesmay only be present in a record in one data store. Thus, a user may berequired to navigate to different pages (e.g., content pages, web pages,network pages, etc.) to access the different data stores and obtain,view, and/or process all of the data corresponding to a particularproperty.

In addition, different data stores may use different identifiers oridentifier formats to reference the same property. Thus, it may bedifficult to determine whether records in different data stores arereferencing the same property or different properties.

SUMMARY

The systems, methods, and devices described herein each have severalaspects, no single one of which is solely responsible for its desirableattributes. Without limiting the scope of this disclosure, severalnon-limiting features will now be discussed briefly.

One aspect of the disclosure provides a system for linking data records.The system comprises a data store comprising a plurality of datarecords. The system further comprises a computing system comprising oneor more computing devices, where the computing system is configured withspecific computer-executable instructions to at least: obtain theplurality of data records; determine that a first data record and asecond data record in the plurality of data records are duplicative;discard the second data record; obtain master record data from a masterrecord data store, where the master record data comprises propertyattribute values for a plurality of real estate properties and uniqueidentifiers associated with individual real estate properties in theplurality of real estate properties; analyze the first record data andthe master record data using a trained machine learning model trainedwith one or more labeled data record pairs; determine that the firstdata record corresponds to a first unique identifier of a first realestate property in the plurality of real estate properties based on theanalysis; identify master record data in the master record data storecorresponding to the first real estate property based on the firstunique identifier; determine that a date associated with the first datarecord is newer than a date associated with the master record data inthe master record data stores that corresponds to the first real estateproperty; and update the master record data in the master record datastore that corresponds to the first real estate property using the firstdata record.

The system of the preceding paragraph can include any sub-combination ofthe following features: where the computing system is further configuredwith specific computer-executable instructions to at least: process aquery for the first real estate property received from a user device,and generate user interface data that, when rendered by the user device,causes the user device to display a user interface, where the userinterface depicts property attribute values of the first real estateproperty; where the master record data indicates that the first realestate property is a child property of a second real estate property inthe plurality of real estate properties, and where the user interfacefurther depicts a slider that, when moved from a date after creation ofthe first real estate property to a date before the creation of thefirst real estate property, causes the user interface to update todepict property attribute values of the second real estate property;where the computing system is further configured with specificcomputer-executable instructions to at least: determine that the firstreal estate property and a second real estate property in the pluralityof real estate properties share a first property attribute value withthe first data record, generate a first feature-based representation ofa combination of property attribute values of the first data record andproperty attribute values of the first real estate property, generate asecond feature-based representation of a combination of propertyattribute values of the first data record and property attribute valuesof the second real estate property, determine that the first data recordand the first real estate property have a match probability above athreshold value based on an application of the first feature-basedrepresentation as an input to the trained machine learning model, anddetermine that the first data record and the second real estate propertyhave a match probability below the threshold value based on anapplication of the second feature-based representation as an input tothe trained machine learning model; where the computing system isfurther configured with specific computer-executable instructions to atleast analyze the first record data and the master record data usingartificial intelligence after failing to identify a match between thefirst data record and the first unique identifier based on a comparisonof property attribute values of the first data record with the masterrecord data; where the computing system is further configured withspecific computer-executable instructions to at least: determine that athird data record in the plurality of data records does not correspondto any unique identifier, generate a third unique identifier for thethird data record, and create a new entry in the master record datastore, where the new entry is associated with the third uniqueidentifier and comprises property attribute values of the third datarecord; where the computing system is further configured with specificcomputer-executable instructions to at least: analyze a third datarecord in the plurality of data records and the master record data usingthe trained machine learning model, obtain an output from the trainedmachine learning model indicating that the third data record correspondsto the first unique identifier and to a second unique identifier of asecond real estate property in the plurality of real estate properties,determine that the trained machine learning model produced a falsepositive result based on the obtained output, generate a third uniqueidentifier for the third data record, and create a new entry in themaster record data store, where the new entry is associated with thethird unique identifier and comprises property attribute values of thethird data record; where the computing system is further configured withspecific computer-executable instructions to at least analyze the firstrecord data and the master record data using artificial intelligencesubsequent to a determination that the first data record comprises aminimum amount of information; and where the computing system is furtherconfigured with specific computer-executable instructions to at least:determine that a third data record in the plurality of data records doesnot comprise a minimum amount of information, and store the third datarecord in a pending file data store in place of attempting to identify aunique identifier that corresponds with the third data record.

Another aspect of the disclosure provides a computer-implemented methodof linking data records. The computer-implemented method comprises: asimplemented by one or more computing devices configured with specificcomputer-executable instructions, obtaining a first data record from adata store via a network; obtaining master record data that comprisesproperty attribute values for a first property and a second property, afirst unique identifier associated with the first property, and a secondunique identifier associated with the second property; analyzing thefirst data record and the master record data using a trained machinelearning model trained with one or more labeled data record pairs;determining that the first data record corresponds to the first uniqueidentifier based on the analysis; determining, subsequent to thedetermination that the first data record corresponds to the first uniqueidentifier, that at least one property attribute value in the first datarecord differs from at least one property attribute value of the firstproperty; and updating the at least one property attribute value of thefirst property using the first data record.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where thecomputer-implemented method further comprises: receiving a query for thefirst property from a user device, and generating user interface datathat, when rendered by the user device, causes the user device todisplay a user interface, where the user interface depicts propertyattribute values of the first property; where the master record dataindicates that the first property is a child property of a thirdproperty, and where the user interface further depicts a slider that,when moved from a date after creation of the first property to a datebefore the creation of the first property, causes the user interface toupdate to depict property attribute values of the third property; whereanalyzing the first data record and the master record data using atiered approach further comprises: determining that the first propertyand the second property share a first property attribute value with thefirst data record, generating a first feature-based representation of acombination of property attribute values of the first data record andproperty attribute values of the first property, generating a secondfeature-based representation of a combination of property attributevalues of the first data record and property attribute values of thesecond property, determining that the first data record and the firstproperty have a match probability above a threshold value based on anapplication of the first feature-based representation as an input to thetrained machine learning model, and determining that the first datarecord and the second property have a match probability below thethreshold value based on an application of the second feature-basedrepresentation as an input to the trained machine learning model; whereanalyzing the first data record and the master record data using atiered approach further comprises analyzing the first record data andthe master record data using artificial intelligence after failing toidentify a match between the first data record and the first uniqueidentifier based on a comparison of property attribute values of thefirst data record with the master record data; where thecomputer-implemented method further comprises: determining that a seconddata record obtained from the data store does not correspond to anyunique identifier, generating a third unique identifier for the seconddata record, and creating a new entry in the master record data, wherethe new entry is associated with the third unique identifier andcomprises property attribute values of the second data record; and wherethe computer-implemented method further comprises: analyzing a seconddata record and the master record data using a trained machine learningmodel, obtaining an output from the trained machine learning modelindicating that the second data record corresponds to the first uniqueidentifier and to the second unique identifier, determining that thetrained machine learning model produced a false positive result based onthe obtained output, generating a third unique identifier for the seconddata record, and creating a new entry in the master record data, wherethe new entry is associated with the third unique identifier andcomprises property attribute values of the second data record.

Another aspect of the disclosure provides non-transitory,computer-readable storage media comprising computer-executableinstructions for linking data records, where the computer-executableinstructions, when executed by a computer system, cause the computersystem to: obtain a first data record, where the first data recordcomprises a first plurality of property attribute values; determine thatthe first data record corresponds to a first unique identifier in aplurality of unique identifiers using a trained machine learning modeltrained with one or more labeled data record pairs, where the firstunique identifier is linked to a second plurality of property attributevalues, the second plurality of property attribute values associatedwith a first property and stored in a master record data store;determine that at least one property attribute value in the firstplurality of property attribute values differs from at least oneproperty attribute value in the second plurality of property attributevalues; and update the at least one property attribute value in thesecond plurality of property attribute values using the first datarecord.

The non-transitory, computer-readable storage media of the precedingparagraph can include any sub-combination of the following features:where the computer-executable instructions further cause the computersystem to: process a query for the first property received from a userdevice, and generate user interface data that, when rendered by the userdevice, causes the user device to display a user interface, where theuser interface depicts property attribute values of the first property;where the master record data indicates that the first property is achild property of a second property, and where the user interfacefurther depicts a slider that, when moved from a date after creation ofthe first property to a date before the creation of the first property,causes the user interface to update to depict property attribute valuesof the second property; and where the computer-executable instructionsfurther cause the computer system to: determine that the trained machinelearning model indicates that a second data record corresponds to thefirst unique identifier and a second unique identifier in the pluralityof unique identifiers, determine that the trained machine learning modelproduced a false positive result, generate a new unique identifier forthe second data record, and update the master record data store toinclude an entry associated with the new unique identifier and thatcomprises property attribute values of the second data record.

Another aspect of the disclosure provides a system for matching datarecords. The system comprises a master record data store configured tostore data associated with a plurality of properties. The system furthercomprises a computing system comprising one or more computing devices,where the computing system is configured to communicate with the masterrecord data store and is configured with specific computer-executableinstructions to at least: process a request received from a user devicevia a network, where the request specifies a data record; identify afirst entity and a second entity in the data record; identify arelationship between the first entity and the second entity; identify acontext associated with the first entity, the second entity, and theidentified relationship using a trained machine learning model trainedwith one or more data records labeled to identify correspondingcontexts; cleanse and standardize data in the data record using one ormore rules selected based on the identified context to form a modifieddata record; determine that the modified data record corresponds to datastored in the master record data store that corresponds to a firstproperty in the plurality of properties; and output an indication of thedetermination such that the user device receives an indication that thedata record matches the first property.

The system of the preceding paragraph can include any sub-combination ofthe following features: where the indication received by the user deviceis a user interface, and where the user interface depicts propertyattribute values of the first property; where the data stored in themaster record data store that corresponds to the first propertyindicates that the first property is a child property of a secondproperty in the plurality of properties, and where the user interfacefurther depicts a slider that, when moved from a date after creation ofthe first property to a date before the creation of the first property,causes the user interface to update to depict property attribute valuesof the second property; where the computing system is further configuredwith specific computer-executable instructions to at least: compare afirst property attribute value in the data record with a range ofacceptable values, determine that the first property attribute value isoutside the range, and remove the first property attribute value fromthe data record; where the computing system is further configured withspecific computer-executable instructions to at least remove extracharacters from the data record; where the computing system is furtherconfigured with specific computer-executable instructions to at least:identify a first entity and a second entity in the data record using afirst trained machine learning model, and identify a relationshipbetween the first entity and the second entity based on an applicationof the first entity and the second entity as inputs to a second trainedmachine learning model; where the one or more data records labeled toidentify corresponding contexts comprises data records in whichentities, relationships between the entities, and the context of therelationships are labeled; where the computing system is furtherconfigured with specific computer-executable instructions to at leastdetermine that the modified data record corresponds to the data storedin the master record data store that corresponds to the first propertybased on a comparison of at least some property attribute values in themodified data record and the data stored in the master record data storethat corresponds to the first property; and where the computing systemis further configured with specific computer-executable instructions toat least: determine that the first property and a second property in theplurality of properties share a first property attribute value with themodified data record, generate a first feature-based representation of acombination of property attribute values of the modified data record andproperty attribute values of the first property, generate a secondfeature-based representation of a combination of property attributevalues of the modified data record and property attribute values of thesecond property, determine that the modified data record and the firstproperty have a match probability above a threshold value based on anapplication of the first feature-based representation as an input to afirst trained machine learning model, and determine that the modifieddata record and the second property have a match probability below thethreshold value based on an application of the second feature-basedrepresentation as an input to the first trained machine learning model.

Another aspect of the disclosure provides a computer-implemented methodof matching data records. The computer-implemented method comprises: asimplemented by one or more computing devices configured with specificcomputer-executable instructions, obtaining a data record; identifying acontext associated with the data record using a trained machine learningmodel trained with one or more data records labeled to identifycorresponding contexts; cleansing data in the data record using one ormore rules selected based on the identified context to form a modifieddata record; standardizing data in the modified data record to form asecond modified data record; determining that the second modified datarecord corresponds to master record data that corresponds to a firstproperty; and causing output of an indication that the data recordmatches the first property.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where the indication is auser interface, and where the user interface depicts property attributevalues of the first property; where the master record data thatcorresponds to the first property indicates that the first property is achild property of a second property, and where the user interfacefurther depicts a slider that, when moved from a date after creation ofthe first property to a date before the creation of the first property,causes the user interface to update to depict property attribute valuesof the second property; where cleansing data further comprises:comparing a first property attribute value in the data record with arange of acceptable values, determining that the first propertyattribute value is outside the range, and removing the first propertyattribute value from the data record; where cleansing data furthercomprises removing extra characters from the data record; wheredetermining that the second modified data record corresponds to masterrecord data that corresponds to a first property further comprisesdetermining that the second modified data record corresponds to themaster record data that corresponds to the first property based on acomparison of at least some property attribute values in the secondmodified data record and the master record data that corresponds to thefirst property; and where determining that the second modified datarecord corresponds to master record data that corresponds to a firstproperty further comprises: determining that the first property and asecond property share a first property attribute value with the secondmodified data record, generating a first feature-based representation ofa combination of property attribute values of the second modified datarecord and property attribute values of the first property, generating asecond feature-based representation of a combination of propertyattribute values of the second modified data record and propertyattribute values of the second property, determining that the secondmodified data record and the first property have a match probabilityabove a threshold value based on an application of the firstfeature-based representation as an input to a first trained machinelearning model, and determining that the second modified data record andthe second property have a match probability below the threshold valuebased on an application of the second feature-based representation as aninput to the first trained machine learning model.

Another aspect of the disclosure provides non-transitory,computer-readable storage media comprising computer-executableinstructions for matching data records, where the computer-executableinstructions, when executed by a computer system, cause the computersystem to: obtain a data record; identify a context associated with thedata record using a trained machine learning model trained with one ormore data records labeled to identify corresponding contexts; modify thedata record using one or more rules selected based on the identifiedcontext to improve a match rate; determine that the modified data recordcorresponds to master record data that corresponds to a first property;and generate an output that causes generation of a representationindicating that the data record matches the first property.

The non-transitory, computer-readable storage media of the precedingparagraph can include any sub-combination of the following features:where the representation is a user interface, and where the userinterface depicts property attribute values of the first property; wherethe master record data that corresponds to the first property indicatesthat the first property is a child property of a second property, andwhere the user interface further depicts a slider that, when moved froma date after creation of the first property to a date before thecreation of the first property, causes the user interface to update todepict property attribute values of the second property; and where thecomputer-executable instructions further cause the computer system to:determine that the first property and a second property share a firstproperty attribute value with the modified data record, generate a firstfeature-based representation of a combination of property attributevalues of the modified data record and property attribute values of thefirst property, generate a second feature-based representation of acombination of property attribute values of the modified data record andproperty attribute values of the second property, determine that themodified data record and the first property have a match probabilityabove a threshold value based on an application of the firstfeature-based representation as an input to a first trained machinelearning model, and determine that the modified data record and thesecond property have a match probability below the threshold value basedon an application of the second feature-based representation as an inputto the first trained machine learning model.

BRIEF DESCRIPTION OF DRAWINGS

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

FIG. 1 is a block diagram of an illustrative operating environment inwhich a data linking system uses artificial intelligence to link datarecords.

FIGS. 2A-2C are flow diagrams illustrating the operations performed bythe components of the operating environment of FIG. 1 to process a datarecord to begin the linking or matching process.

FIGS. 3A-3B are flow diagrams illustrating the operations performed bythe components of the operating environment of FIG. 1 to link or matchdata records to unique identifiers (e.g., to link or match data recordsto a representation of a specific property).

FIG. 4 is a flow diagram illustrating the operations performed by thecomponents of the operating environment of FIG. 1 to provide propertydata to a user device for display in an interactive user interface.

FIGS. 5A-5B illustrate an example user interface depicting propertyattribute values for a property requested by a user via a user device.

FIG. 6 illustrates another example user interface depicting propertyattribute values for a property requested by a user via a user device.

FIG. 7 is a more detailed block diagram of the entity matching engine ofFIG. 1.

FIGS. 8A-8B are flow diagrams illustrating the operations performed bythe components of the entity matching engine of FIGS. 1 and 7 to preparea data record to be matched and to perform the matching.

FIG. 9 is a flow diagram depicting a data linking routine illustrativelyimplemented by a data linking system, according to one embodiment.

FIG. 10 is a flow diagram depicting a data matching routineillustratively implemented by an entity matching engine, according toone embodiment.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

As described above, data corresponding to a particular real estateproperty (e.g., a parcel, a lot, etc.) may be stored in many differentdata stores accessible via a network. The data stores may storedifferent information associated with a property, and therefore a useroperating a user device may be required to perform additionalnavigational steps to obtain, view, and/or process all of the datacorresponding to a particular property. For example, a computing systemthat maintains a data store may provide a network-accessible interfaceby delivering a content page (e.g., a network page, a web page, etc.) tovarious user devices that allows such user devices to search the datastore. Because different data stores may store different information, auser may have to open different browser tabs and/or windows and navigateto multiple content pages to obtain all of the data corresponding to aparticular property.

In some cases, a user may not be able to access all of the datacorresponding to a particular property even if the user is able toaccess the different data stores. For example, there can bediscrepancies in the data, such as misspelled names, incorrectaddresses, conflicting values for the number of bedrooms in theproperty, etc. Thus, if a user device submits a query for dataassociated with a property using the correct address for the property,but queries a data store that includes a record with an incorrectaddress for the property and/or a record that incorrectly identifies adifferent property as having the address, the query may yield no resultsor data for the wrong property.

As another example, properties change over time. For example, a lot maybe subdivided to create multiple sublots, with each sublot having adifferent address, parcel number, owner, etc. than the original lot. Asanother example, a lot may be merged with another lot to create a largerlot, with the larger lot having a different address, parcel number,owner, etc. than the original lots that formed the merged lot.Conventional systems that manage the data stores described above have nomechanism for linking or otherwise indicating a relationship between theoriginal lot(s) and the new lot(s). Thus, an original lot and a new lotmay appear to be different properties altogether. While a user devicemay submit a query for a new lot, data associated with the original lotmay be useful as well (e.g., to determine how property values havechanged over time, to determine how a certain area has developed overtime, etc.). However, a query of the data store for information on thenew lot may only yield results for the new lot.

In other cases, a query of the data stores may yield duplicativeinformation that can increase processing times. For example, two datastores may include records for a property providing values for the sameproperty attributes. However, each data store may use differentterminology to describe the various property attributes. For example,one data store may refer to the bedroom number attribute as “BDR” andanother data store may refer to the bedroom number attribute as “BDRMS.”Some conventional systems may be able to consolidate data from differentdata stores based on common property attributes. However, theseconventional systems may not be able to consolidate or deduplicate thedata when otherwise common property attributes are referenced usingdifferent terminology. Thus, a user device that submits queries todifferent data stores may receive duplicative data. The user device orother computing system that further processes the received data (e.g.,to generate reports, user interfaces, etc.) may not identify theduplicated data given the different terminology. Accordingly, the sameprocessing steps may be performed on the same data values multipletimes, thereby increasing the processing time for performing the furtherprocessing.

Accordingly, aspects of the present disclosure provide a data linkingsystem that uses artificial intelligence to link data recordscorresponding to a particular property even if there are inconsistenciesin the data records, the physical presence of the property has changedover time (e.g., the property has been subdivided, merged, becomeinactive, etc.), and/or the data records use different terminology. Thedata linking system can optionally visualize the data record linkage viainteractive user interfaces. By linking data records despite the issuesdescribed above, the data linking system reduces the number ofnavigational steps a user performs to obtain data associated with aproperty and/or reduces data processing times.

The disclosed data linking system and methods can be used to generateand maintain a comprehensive, authoritative database of substantiallyall properties (or all properties of a particular type, such asresidential properties, commercial properties, industrial properties,etc.) within a jurisdiction (e.g., the United States), with eachproperty assigned a unique identifier. As used herein, a “property” maybe any residential, commercial, and/or industrial real estate propertyowned by a private and/or governmental entity.

While the primary use case for the data linking system described hereinis for residential, commercial, and/or industrial real estate propertydata records, this is not meant to be limiting. For example, the datalinking system can use similar techniques to link other types of datarecords, such as vehicle records, media records (e.g., music records,television show records, movie records, etc.), financial records (e.g.,securities records, bank records, etc.), item purchase records, and/orthe like.

The foregoing aspects and many of the attendant advantages of thisdisclosure will become more readily appreciated as the same becomebetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings.

Example Data Linking Environment

FIG. 1 is a block diagram of an illustrative operating environment 100in which a data linking system 120 uses artificial intelligence to linkdata records. The operating environment 100 further includes one or morethird party data stores 130 that may communicate with the data linkingsystem 120 via a network 110 to provide data records. Furthermore, theoperating environment 100 includes various user devices 102 that maycommunicate with the data linking system 120 to obtain and view linkeddata records.

The data linking system 120 can be a computing system configured toobtain data records from one or more third party data stores 130,identify to which properties the obtained data records belong, linktogether data records corresponding to the same property, and/orgenerate user interface data that, when rendered by a user device 102,causes the user device 102 to display a user interface that visuallydepicts data records corresponding to a particular property. Forexample, the data linking system 120 can assign a unique identifier toan individual property. Once a data record is identified ascorresponding to a particular property, the data linking system 120 canlink the data record to the property's unique identifier. In response toa user device 102 query for data associated with a particular property,the data linking system 120 can then identify the unique identifier forthe property, and then retrieve and provide the data records linked tothe unique identifier.

If a property assigned a unique identifier is subdivided or otherwisesplit into one or more other properties, the data linking system 120 mayassign the new properties new unique identifiers. However, as describedin greater detail below, the data linking system 120 may link the uniqueidentifier of the original property with the unique identifiers of thenew, “child” properties resulting from the subdivision. Thus, the datalinking system 120 can create a hierarchy of unique identifiers. Inresponse to a user device 102 query for data associated with a childproperty, the data linking system 120 can then use the linked, hierarchyof unique identifiers to retrieve data records for the child propertyand for the original, parent property.

The data linking system 120 may be a single computing device, or it mayinclude multiple distinct computing devices, such as computer servers,logically or physically grouped together to collectively operate as aserver system. The components of the data linking system 120 can each beimplemented in application-specific hardware (e.g., a server computingdevice with one or more ASICs) such that no software is necessary, or asa combination of hardware and software. In addition, the modules andcomponents of the data linking system 120 can be combined on one servercomputing device or separated individually or into groups on severalserver computing devices. In some embodiments, the data linking system120 may include additional or fewer components than illustrated in FIG.1.

In some embodiments, the features and services provided by the datalinking system 120 may be implemented as web services consumable via thecommunication network 110. For example, the entire data linking system120 may be implemented as a web service, or individual component(s) ofthe data linking system 120 (such as the entity matching engine 124,described below) may be implemented as individual web services. Infurther embodiments, the data linking system 120 is provided by one morevirtual machines implemented in a hosted computing environment. Thehosted computing environment may include one or more rapidly provisionedand released computing resources, which computing resources may includecomputing, networking and/or storage devices. A hosted computingenvironment may also be referred to as a cloud computing environment.

The data linking system 120 may include various modules, components,data stores, and/or the like to provide the data record linkingfunctionality described herein. For example, the data linking system 120may include a data analyzer 121, a data deduper 122, a minimum viablerecord (MVR) checker 123, an entity matching engine 124, a recordupdater 125, an ID generator 126, a user interface generator 127, apending file data store 128, and a master record data store 129.

The data analyzer 121 can obtain one or more data records from one ormore of the third party data stores 130 via the network 110. The thirdparty data stores 130 may be operated by third party entities (e.g., anentity other than the entity that operates the data linking system 120)and/or by the same entity as the entity that operates the data linkingsystem 120, and may store tax roll data, property transaction data(e.g., deed, mortgage, releases, foreclosures, etc.), appraisal data,listing data, and/or any other type of data that describes a property orthe events that have occurred in relation to a property (e.g., a sale, aforeclosure, a subdivision, etc.). The data records may include valuesfor zero or more of the following property attributes: tax/legal fields(e.g., assessor parcel number (APN), subdivision, lot number, tractnumber, block number, etc.), SITUS (e.g., physical street address,geographic coordinates like latitude and longitude, etc.), legal partynames (e.g., owner, seller, buyer, etc.), structure information (e.g.,number of bedrooms, number of bathrooms, year built, etc.), and/or value(e.g., tax assessment, appraisal value, listing price, etc.). Whileseveral property attributes are listed above, this is not meant to belimiting. Any number of types of attributes can be processed by the datalinking system 120 in the same manner as described herein link datarecords.

Once the data records are obtained, the data analyzer 121 can determinewhether a data record is a standalone data record or an incrementalupdate data record. For example, the data analyzer 121 may obtain afirst data record (e.g., a standalone data record) that has incorrectand/or incomplete data. An entity managing the third party data store130 from which the first data record is obtained may notice the error,providing the data linking system 120 with an updated data record (e.g.,an incremental update data record) that corrects the issue with thefirst data record. The data analyzer 121 may identify an incrementalupdate data record because the incremental update data record may beprovided with an indication of a previous, standalone data record thatthe incremental update data record updates. A standalone data record canbe any data record other than an incremental update data record.

If the data analyzer 121 determines that an obtained data record is astandalone data record, then the data analyzer 121 can pass the datarecord to the data deduper 122. Otherwise, if the data analyzer 121determines that an obtained data record is an incremental update datarecord, then the data analyzer 121 can use the information associatedwith the incremental update data record to identify the data record thatthe incremental update data record is updating, retrieve the identifieddata record from the pending file data store 128 (or the master recorddata store 129), update the identified data record, and transmit theupdated data record to the data deduper 122.

The data deduper 122 can perform mapping and/or deduping functionality.For example, one or more data records obtained from the third party datastore(s) 130 may reference the same property. Thus, the data linkingsystem 120 could generate one unique identifier for these data records,instead of multiple unique identifiers for each data record, to reduceprocessing. Accordingly, the data deduper 122 can analyze data recordsobtained from the third party data store(s) 130, identifying those datarecords that correspond to the same property. For example, the datadeduper 122 can analyze property attribute values (e.g., SITUS, legalparty name, etc.) included in the data records, identifying data recordsas corresponding to the same property if the data records include thesame property attribute values. Of those data records that correspond tothe same property, the data deduper 122 may send one of the data recordsto the MVR checker 123. The data deduper 122 may retain the remainingdata records, eventually linking these data records to a uniqueidentifier that is eventually generated in association with the datarecord sent to the MVR checker 123 or a unique identifier that alreadyexists and corresponds with the property.

Prior to sending a data record to the MVR checker 123, the data deduper122 may standardize the data record. For example, a data record may bespread out over several tables. The data deduper 122 can identify theproperty attributes used by the other components of the data linkingsystem 120 (e.g., the property attributes listed above), and consolidatethose property attributes of the data record into a single table. Thedata deduper 122 can also standardize column names (e.g., propertyattribute names), data types, and/or the formatting of the propertyattribute values. In some cases, different third party data stores 130use different types of values for the same property attribute. Thus, thedata deduper 122 can convert the values included in data recordsobtained from certain third party data stores 130 (e.g., appraisal datastores) into a standard format. By standardizing the data record, thedata deduper 122 may help improve the accuracy of the matching performedby the entity matching engine 124 discussed below. Improving theaccuracy of the matching performed by the entity matching engine 124 canreduce the number of situations in which multiple unique identifiers areassigned to the same property, which thereby reduces any furtherprocessing performed by the data linking system 120 or a user device102.

The MVR checker 123 may determine whether a data record received fromthe data deduper 122 includes enough information to identify a propertycorresponding to the data record. For example, the MVR checker 123 maydetermine that the data record has enough information to identify aproperty corresponding to the data record if the data record includes atleast the name of the property owner and some indication of physicalboundaries of the property. A physical boundary of the property can berepresented by an APN, a SITUS, a legal description (e.g., as recordedin a recorder's office), or a physical boundary (e.g., a parcelboundary, a building structure footprint, etc.).

In some cases, the name of the property owner may be confidential. Thus,the MVR checker 123 may determine that a data record has enoughinformation to identify a property corresponding to the data record ifthe data record includes an APN and originates from a third party datastore 130 that is a tax roll data store. In addition, the MVR checker123 may determine that a data record has enough information to identifya property corresponding to the data record if the data record includesonly a SITUS and originates from a third party data store 130 that is alisting data store.

If the MVR checker 123 determines that a data record has enoughinformation to identify a property corresponding to the data record, theMVR checker 123 passes the data record to the entity matching engine124. Otherwise, if the MVR checker 123 determines that a data recorddoes not have enough information to identify a property corresponding tothe data record, the MVR checker 123 stores the data record in thepending file data store 128. This data record may then be reanalyzed bythe data linking system 120 after, for example, an incremental updatedata record is received to update the data record with more information.

The entity matching engine 124 may determine whether a data recordreceived from the MVR checker 123 corresponds to a property that hasalready been assigned a unique identifier or to a property that has notbeen assigned a unique identifier. It may be important for the entitymatching engine 124 to be as accurate as possible in identifying matchesbetween a received data record and a property assigned a uniqueidentifier (e.g., it may be important for the entity matching engine 124to have a high match rate) because the higher number of data recordsthat are accurately identified as being associated with a uniqueidentifier that has already been generated, the fewer number ofprocessing resources that may be expended by the data linking system120, a user device 102, and/or an external system (not shown) in thefuture when further processing data stored in and obtained from themaster record data store 129.

The entity matching engine 124 may use a tiered approach to make thedetermination. For example, in a first tier, the entity matching engine124 determines whether the data record includes an APN and at least oneof a SITUS, a legal field, or an owner name. If the data record includesthis information, the entity matching engine 124 queries the masterrecord data store 129 to determine whether any data record storedtherein includes matching property attribute values. Each data recordstored in the master record data store 129 may correspond to a uniqueproperty and include an indication of the unique identifier assigned tothe property. Thus, if the query yields a match (e.g., there is a datarecord stored in the master record data store 129 that includes the sameAPN and the same SITUS, legal field, or owner name as the data recordbeing processed by the entity matching engine 124), then the entitymatching engine 124 can identify the unique identifier assigned to theproperty to which the data record corresponds. The entity matchingengine 124 can then forward the data record and/or unique identifier tothe record updater 125.

In some embodiments, the entity matching engine 124 can use fuzzy logicin the first tier to increase the match rate. For example, the entitymatching engine 124 can determine that the owner name in a data recordmatches the owner name in a data record stored in the master record datastore 129 if the first N number of characters in the name match (e.g.,1, 2, 3, 4, 5, etc.), if at least the last names match, if the names areotherwise related (e.g., based on an identified family relationship), ifthe street numbers partially match (e.g., the first two numbers match,the last two numbers match, the first and last numbers match, etc.), ifat least the street name matches, if there is a partial match of thelegal information (e.g., at least 1 character matches), and/or the like.

However, if the data record does not include the APN and at least one ofa SITUS, a legal field, or an owner name and/or if the query yields nomatch (or no partial match based on fuzzy logic), then the entitymatching engine 124 attempts to find a match using a second tier. In thesecond tier, the entity matching engine 124 determines whether the datarecord includes a SITUS (e.g., both physical street address andgeographic coordinates) and the name of an owner. If the data recordincludes this information, the entity matching engine 124 determineswhether the SITUS passes a standardization test. The entity matchingengine 124 may ensure that the SITUS is standardized to increase theoverall match rate. For example, the entity matching engine 124 candetermine whether the length of a portion of the SITUS (e.g., a zipcode) is greater than a threshold number of characters, whether theSITUS can be geomatched to a location in a specific format, and/or thelike. The standardization test that the entity matching engine 124applies to the data record can be different based on which third partydata store 130 from which the data record originates. If the data recordpasses the standardization test, the entity matching engine 124 queriesthe master record data store 129 to determine whether any data recordstored therein includes matching property attribute values. If the queryyields a match (e.g., there is a data record stored in the master recorddata store 129 that includes the same SITUS and owner name as the datarecord being processed by the entity matching engine 124), then theentity matching engine 124 can identify the unique identifier assignedto the property to which the data record corresponds. The entitymatching engine 124 can then forward the data record and/or uniqueidentifier to the record updater 125.

If the data record does not include the SITUS and an owner name, thequery yields no match, and/or the SITUS does not pass thestandardization test, then the entity matching engine 124 attempts tofind a match using a third tier. In the third tier, the entity matchingengine 124 determines whether the data record includes a SITUS,structure information (including a year built), and a value (e.g.,market value, sale price, listing price, appraised value, etc.). If thedata record includes this information, the entity matching engine 124determines whether the SITUS passes a standardization test. For example,the entity matching engine 124 can determine whether the length of aportion of the SITUS (e.g., a zip code) is greater than a thresholdnumber of characters, whether the SITUS can be geomatched to a locationin a specific format, and/or the like. If the data record passes thestandardization test, the entity matching engine 124 queries the masterrecord data store 129 to determine whether any data record storedtherein includes matching property attribute values. If the query yieldsa match (e.g., there is a data record stored in the master record datastore 129 that includes the same SITUS, same structure information, andsimilar value (e.g., at least within a threshold percentage of eachother) as the data record being processed by the entity matching engine124), then the entity matching engine 124 can identify the uniqueidentifier assigned to the property to which the data recordcorresponds. The entity matching engine 124 can then forward the datarecord and/or unique identifier to the record updater 125.

If the data record does not include the SITUS, the structureinformation, and the value, the query yields no match, and/or the SITUSdoes not pass the standardization test, then the entity matching engine124 attempts to find a match using a fourth tier. In the fourth tier,the entity matching engine 124 determines whether the data recordincludes a SITUS (e.g. both a physical street address and geographiccoordinates). If the data record includes this information, the entitymatching engine 124 determines whether the SITUS passes astandardization test. For example, the entity matching engine 124 candetermine whether the length of a portion of the SITUS (e.g., a zipcode) is greater than a threshold number of characters, whether theSITUS can be geomatched to a location in a specific format, and/or thelike. If the data record passes the standardization test, the entitymatching engine 124 queries the master record data store 129 todetermine whether any data record stored therein includes matchingproperty attribute values. If the query yields a match (e.g., there is adata record stored in the master record data store 129 that includes thesame SITUS as the data record being processed by the entity matchingengine 124), then the entity matching engine 124 can identify the uniqueidentifier assigned to the property to which the data recordcorresponds. The entity matching engine 124 can then forward the datarecord and/or unique identifier to the record updater 125.

If the data record does not include the SITUS, the query yields nomatch, and/or the SITUS does not pass the standardization test, then theentity matching engine 124 attempts to find a match using a fifth tier.In the fifth tier, the entity matching engine 124 uses artificialintelligence (e.g., machine learning) to attempt to find a match. Forexample, the entity matching engine 124 can query the master record datastore 129 and identify some or all of the data records stored thereinthat include the same first five digits of the zip code as the datarecord to be matched and that include the same street number as the datarecord to be matched. The entity matching engine 124 can then createdata structures in which the data record to be matched is individuallypaired with some or all of the identified data records stored in themaster record data store 129. Thus, if 10 data records stored in themaster record data store 129 are identified as having the same firstfive digits of the zip code and the same street number as included inthe data record to be matched, the entity matching engine 124 can create10 pairs.

The entity matching engine 124 can then convert each pair into afeature-based representation. For example, each pair may be convertedinto a vector, where each element in the vector represents a feature andcorresponds to a property attribute. The value of the element may dependon a comparison of the data record obtained from the master record datastore 129 and the data record to be matched. For example, for propertyattribute values that are alphanumeric values and/or for some propertyattribute values that are numeric values (e.g., street name, streetaddress, city, state, owner name, year built, tax assessment value,listing price, subdivision name, lot number, etc.), the entity matchingengine 124 may set the corresponding element value to be −1 (or anotherinteger) if the value of the property attribute in the master recorddata store 129 data record and/or in the data record to be matched valueis empty (e.g., no value is provided for a street name in one or bothdata records) or to a value representing a distance between the valuesin the master record data store 129 data record and in the data recordto be matched (e.g., the element value may be a length-normalizedLevenshtein distance between the value in the master record data store129 data record and the value in the data record to be matched).Alternatively, some or all property attributes may be associated with anattribute_exist flag (e.g., street_name_exist_flag,street_address_exist_flag, etc.) that can be set to −1 (or anotherinteger) instead of the property attribute value itself if the propertyattribute value in the data record is otherwise empty. The entitymatching engine 124 can also determine a distance between a seller'sname included in the data record to be matched and an owner's nameincluded in the master record data store 129 data record.

For other property attribute values that are numeric values (e.g., lotsize, number of rooms, number of bedrooms, number of bathrooms, numberof fireplaces, size of living area, year built, number of stores, taxassessed value, listing price, etc.), the entity matching engine 124 mayset the corresponding element value to be −1 (or another integer) if thevalue of the property attribute in the master record data store 129 datarecord and/or in the data record to be matched is null, or to a valuerepresenting an absolute value of the difference between the values inthe master record data store 129 data record and in the data record tobe matched. For latitude and longitude values, the entity matchingengine 124 can set the corresponding element value to the numericdistance between the latitude and longitude of the master record datastore 129 data record and the data record to be matched.

For any property attributes that have binary values (e.g., whether apool is present), the entity matching engine 124 can set thecorresponding element value to the XOR of the values in the masterrecord data store 129 data record and in the data record to be matched(e.g., the element value is 1 if both data records indicate that a poolis present or that a pool is not present, and is 0 if the data recordsgive opposite indications).

The entity matching engine 124 can then apply each feature-basedrepresentation as an input to a machine learning model. In response, themachine learning model may output a probability of a match (e.g., aprobability that a master record data store 129 data record and a datarecord to be matched, which are paired and represented by thefeature-based representation, correspond to the same property). Theentity matching engine 124 may identify a match if the probability isabove a threshold value (e.g., 50%). If the entity matching engine 124identifies a match, the entity matching engine 124 can identify theunique identifier associated with the master record data store 129 datarecord that resulted in a match, and forward the data record to bematched and/or the unique identifier to the record updater 125.

Prior to using the machine learning model to identify potential matches,the entity matching engine 124 may have trained the machine learningmodel. For example, the entity matching engine 124 may obtain a large,synthetic dataset that includes data records that have been matchedusing any of the first four tiers. The entity matching engine 124 maythen pair these data records and generate feature-based representationsusing the techniques described above. Because the data records in thedataset have already been matched, each feature-based representation isalso labeled with an indication that the data records are a match or arenot a match. The entity matching engine 124 uses a portion of thelabeled feature-based representations to train the machine learningmodel, and another portion of the labeled feature-based representationsto evaluate the accuracy of the machine learning model (e.g., by feedingthe feature-based representations as inputs to the machine learningmodel and determining whether the resulting probability is above athreshold value when a match should be identified and is below athreshold value when a match should not be identified). Alternatively orin addition, the entity matching engine 124 can take in user feedback totrain or update the machine learning model (e.g., the user feedback caninclude an indication of when a match was properly identified, when amatch was improperly identified, when no match was properly identified,and/or when no match was improperly identified).

One issue, however, with using machine learning to increase the matchrate of the entity matching engine 124 is that the machine learningmodel may produce false positives (e.g., data records that are matchedwhen such data records should not be matched). This may occur when twodistinct properties share the same characteristics, and the specificdata records involved in the match are missing those few propertyattributes that would allow the machine learning model to identify amismatch rather than a match. As an illustrative example, homes locatedwithin the same subdivision may have identical floor plans, andtherefore many of the property attributes values (e.g., number ofbedrooms, number of bathrooms, lot size, etc.) may be the same. If thestreet address, for example, is missing from the data records, themachine learning model may generate a false positive.

Thus, the entity matching engine 124, after receiving an output from themachine learning model, may implement additional matching operations inan attempt to reduce the number of false positives generated by themachine learning model, thereby improving the functionality of the datalinking system 120 itself. For example, the entity matching engine 124may determine that a match identified by the machine learning model isindeed a match if a data record to be matched matches to a uniqueproperty in the master record data store 129. In other words, the entitymatching engine 124 may determine that a match identified by the machinelearning model is not a match if the machine learning model indicatesthat the data record to be matched matches to a first data record in themaster record data store 129 and any number of other data records in themaster record data store 129 (e.g., the machine learning model produceda high probability score, such as over 50%, for more than onefeature-based representation). If the entity matching engine 124identifies a false positive, then the entity matching engine 124 doesnot send the data record and/or unique identifier to the record updater125 even though the machine learning model identifies a match. Theseadditional matching operations allows the entity matching engine 124 toovercome the myopic “one pair at a time” approach taken by the machinelearning model, allowing the entity matching engine 124 to consider allrelated pairs as a group in making a final determination as to whether amatch is present.

In some embodiments, the entity matching engine 124 can also receiveuser feedback to reduce the number of false positives generated by themachine learning model. For example, feedback provided by users mayindicate which matches were accurate and which were inaccurate. Themachine learning model can then be tuned using this feedback to reducethe false positive rate going forward.

The entity matching engine 124 can assign a confidence score to aresulting match based on the tier that results in a match. For example,a match resulting in the first tier may be assigned a highest confidencescore, whereas a match resulting in the fifth tier may be assigned alowest confidence score. The confidence score can be associated with adata record (or the property attribute values included therein) andstored in the master record data store 129. Thus, a user device 102 can,for example, request data records from the data linking system 120 thatare associated with confidence scores higher than a threshold value, ata threshold value, less than a threshold value, etc. If none of thetiers implemented by the entity matching engine 124 yields a match, thenthe entity matching engine 124 forwards the data record to the IDgenerator 126.

For example, the confidence score associated with a tier may bedependent on a false positive rate and/or a false negative rateassociated with the tier. As an illustrative example, the first tier mayhave a 0% false positive rate and a 0% false negative rate and may beassociated with a confidence score of 10. The fifth tier may have a 5%false positive rate and a 0.1% false negative rate and may be associatedwith a confidence score of 8. When a user device is provided with anindication of a possible match, the provided information may beannotated, modified, or otherwise labeled to indicate the correspondingconfidence score of the match (e.g., the confidence score associatedwith the tier that produced the match).

Furthermore, the confidence score associated with a tier may changedepending one or more factors. For example, such factors can include adata source (e.g., tax roll, listing data, etc.), location of theproperty (e.g., whether the location is a slow developing area or a fastdeveloping area), land use code of the property (e.g., single-familyhouse, multi-family house, etc.), and/or the like. Thus, even if twodifferent matches resulted from the same tier, the confidence scoresassociated with the matches may be the same or different. In someembodiments, a machine learning model can be trained (e.g., by theentity matching engine 124) to estimate the accuracy of each tier giventhe factors described above (e.g., by using a training set of data thatincludes matches produced by a tier, an indication of the factorscorresponding to each match, and labels identifying the accuracy ofthese matches).

In further embodiments, as discussed below with respect to FIG. 7, theentity matching engine 124 can perform one or more pre-processingoperations prior to implementing the tiered approach to identifying amatch. The pre-processing operations may include further cleansingand/or standardizing of the data record, which can further increase theentity matching engine 124 match rate (and thereby reduce the amount offurther processing performed by the data linking system 120, a userdevice 102, and/or an external system, not shown).

The record updater 125 can determine whether the data record obtainedfrom the entity matching engine 124 includes data newer than the datastored in the master record data store 129 data record. For example, themaster record data store 129 may include several tables, including amaster record history table, a master record table, a mastercross-reference table, and/or a master event history table. Each entryin the master record history table may be associated with a uniqueidentifier and indicate the property record history of the propertycorresponding to the unique identifier (e.g., where the property recordhistory includes information identify the date a deed was recorded, thedate a refinance occurred, the date a remodel was completed, etc.).Thus, the record updater 125 can query the master record history tableusing the unique identifier provided by the entity matching engine 124,and identify the most-recent date indicated in the master record historytable entry for the unique identifier. The record updater 125 can thenidentify a date included in the data record obtained from the entitymatching engine 124, and compare the most-recent date with the datarecord date to determine which is most-recent. If the date included inthe data record is newer, then the record updater 125 can update themaster record table as described below. Otherwise, if the date includedin the data record is not newer, then the record updater 125 can updatethe master record history table to include the date of the data recordobtained from the data linking system 120 and an indication of a type ofthe data record (e.g., a listing, an appraisal, a transaction, etc.).

As for the other tables in the master record data store 129, each entryin the master record table may be associated with a unique identifierand include the most-recent property attribute values of the propertycorresponding to the unique identifier. The entries in the master recordtable are also referred to above as master record data store 129 datarecords. Each entry in the master cross-reference table may indicate alinkage between a unique identifier and each data record obtained from athird party data store 130 determined by the data linking system 120 tocorrespond to the unique identifier. Each entry in the master eventhistory table may indicate events corresponding to a unique identifier,such as a date that a property corresponding to the unique identifierwas created, a date that a property corresponding to the uniqueidentifier was updated, a date that a property corresponding to theunique identifier was split into multiple sub-properties or was mergedto create a larger property, a date that a property corresponding to theunique identifier was deactivated (e.g., the property was damaged ordestroyed), etc. While the master record data store 129 is described ashaving four tables, this is not meant to be limiting. The data includedin the four tables can be combined into three or fewer tables, orfurther split into five or more tables.

Thus, if the record updater 125 determines that the data record obtainedfrom the data linking system 120 is newer, the record updater 125 canquery the master record table for an entry associated with the uniqueidentifier obtained from the entity matching engine 124. The recordupdater 125 can then determine whether any property attribute valuesincluded in the data record are different from the property attributevalues in the master record table entry and, if so, update the masterrecord table entry to include the property attribute values included inthe data record. The operations performed by the data linking system 120to consolidate information from many different data recordscorresponding to a property into a single entry or data record in themaster record table can (1) reduce future processing operationsperformed by the data linking system 120 because the data linking system120 now does not have to retrieve and provide each of the data recordsin response to a query submitted by a user device 102 for propertyinformation; (2) reduce future processing operations performed by a userdevice 102 and/or an external system (not shown) because, in response toa query for property information, the user device 102 and/or externalsystem receives a curated set of property attribute values rather thanmultiple sets of property attribute values spread out over multiple datarecords, where the property attribute values could be outdated orotherwise incorrect; and (3) reduce data linking system 120 memory usagebecause the master record data store 129 may not have to store each datarecord obtained from the third party data store 130—rather, the masterrecord data store 129 can merely store the most-recent or up-to-dateproperty attribute values of a given property.

Once the master record history table and/or the master record table areupdated, the record updater 125 can also update the mastercross-reference table to link, in the table, the data record obtainedfrom the entity matching engine 124 with the unique identifier obtainedfrom the entity matching engine 124. For example, the data recordsstored in the various third party data stores 130 may have a referenceidentifier, and the master cross-reference table can link the uniqueidentifier to the reference identifiers. Thus, if a user device 102submits a query to the data linking system 120 for some or all of thedata records corresponding to a particular property, the data linkingsystem 120 can query the master cross-reference table and quicklyidentify the relevant data records, providing the reference identifiersto the user device 102. The user device 102 can then request some or allof the data records from the appropriate third party data stores 130.

In some embodiments, the data record obtained from the entity matchingengine 124 may indicate an event that occurred in relation to theproperty (e.g., creation, update, split, merge, deactivation, etc.). Therecord updater 125 can analyze the data record obtained from the entitymatching engine 124 to identify such a situation, and update the masterevent history table accordingly. If the record updater 125 identifiesthat a property has been split or merged, the record updater 125 cansend data (e.g., property attribute values) associated with the propertyor properties formed by the split or merge to the ID generator 126. Oncethe ID generator 126 generates a new unique identifier for a split ormerged property, the ID generator 126 can update the master eventhistory table to link the unique identifier of the property that wassplit or merged with the unique identifier(s) of the resulting propertyor properties. As described below, this linking of unique identifiersallows the user interface generator 127 to then generate an interactiveuser interfaces that allow a user to view how a property has changedover time.

The ID generator 126 can generate a new unique identifier for a propertyassociated with a data record obtained from the entity matching engine124 (since the entity matching engine 124 provides the data record tothe ID generator 126 when a match is not found). For example, the IDgenerator 126 can use a random number generator to assign a uniqueidentifier to the data record (where the ID generator 126 performs acheck to ensure that the generated random number is unique and has notbeen assigned to other data records or properties). Alternatively, theID generator 126 can generate unique identifiers in sequence, assigningthe next unique identifier in the sequence to the data record. Theunique identifier can be any number of digits, such as 10, 20, 30, etc.Once created, the ID generator 126 can create a new entry in the masterrecord history table, the master record table, the mastercross-reference table, and/or the master event history table for thegenerated unique identifier, populating one or more of the tables withproperty attribute values included in the data record.

In some embodiments, the ID generator 126 can generate anidentifiability score associated with a unique identifier that estimatesthe confidence of creating a new unique identifier based on a datarecord from any data source. Data completeness, data accuracy, and/ordata source reliability may be factors that affect the identifiabilityscore. The identifiability score can be provided to a user device 102 inconjunction with other information related to the corresponding uniqueidentifier (e.g., property data), thereby allowing a user to get a moreaccurate estimate on the population and growth of the real estate marketfor a portfolio or region of interest. As an illustrative example, theidentifiability score may indicate that in a first region, there is aconfidence of 99% that there has been a 2% increase in the number ofsingle family houses in the first region, and there is a confidence of70% that there has been a 4% increase in the number of single familyhouses in the first region.

As an example, an identifiability score associated with a uniqueidentifier may be high if a new property is recognized from tax rolldata, regardless of whether the SITUS is complete. An identifiabilityscore associated with a unique identifier may also be high if a newproperty is recognized from listing, appraisal, or transaction data, isnot found in the tax roll data, but the SITUS has been verified by athird party data service (e.g., a USPS address database). Anidentifiability score associated with a unique identifier may be mediumif a new property is recognized from listing data and has a SITUSverified by appraisal or transaction data, but is not found in the taxroll data and the SITUS is not verified by the third party data service.An identifiability score associated with a unique identifier may also bemedium if a new property is recognized from appraisal data and a legalsubdivision and/or lot number is verified by transaction data, but isnot in the tax roll data and the SITUS is not verified by the thirdparty data service. An identifiability score associated with a uniqueidentifier may be low if a new property is recognized from thetransaction, listing, or appraisal data, but is not found in the taxroll data and the SITUS is not verified by the third party data service.

In some embodiments, the identifiability score associated with a uniqueidentifier is not static. For example, the identifiability scoreassociated with a unique identifier may change as property recordsassociated with the unique identifier are updated. As an illustrativeexample, an identifiability score for a unique identifier may initiallybe low because the SITUS could not be verified. After an update in whichthe SITUS is corrected, updated to be more complete, etc. and can now beverified, the identifiability score may be updated (e.g., to a highervalue) to reflect the fact that the SITUS can be verified. As anotherillustrative example, the third party data service may be out-of-date(e.g., for a newly built neighborhood) and thus the identifiabilityscore associated with a unique identifier may be low because the SITUScould not be verified. Once the third party data service is updated, thesame SITUS can now be verified, and thus the identifiability score maybe updated (e.g., to a higher value).

The user interface generator 127 can generate user interface data that,when rendered by a user device 102, causes the user device to display auser interface in which various property attribute values of a propertyare displayed, optionally including interactive features that allow auser to view the history of a property over time. The user interfacesare described in greater detail below with respect to FIGS. 5A through6.

The pending file data store 128 stores data records that are incompleteor otherwise do not qualify as including a minimum amount of informationto potentially identify a match to a unique identifier (e.g., to aproperty). While the pending file data store 128 is depicted as beinglocated internal to the data linking system 120, this is not meant to belimiting. For example, not shown, the pending file data store 128 can belocated external to the data linking system 120.

The master record data store 129 stores various tables corresponding toa master record of property attribute values, as described above. Whilethe master record data store 129 is depicted as being located internalto the data linking system 120, this is not meant to be limiting. Forexample, not shown, the master record data store 129 can be locatedexternal to the data linking system 120.

The speed at which the data linking system 120 (and/or the entitymatching engine 124) perform the operations described herein may befaster than conventional systems given the architecture of the datalinking system 120 (and/or the entity matching engine 124). As anillustrative example, the data linking system 120 (and/or the entitymatching engine 124) can process and link over 1 billion data records inless than 4 hours. Conventional systems, on the other hand, may takemultiple days to process and link the same volume of data records. Thedata linking system 120 (and/or the entity matching engine 124) canachieve such results using a distributed parallel processing approach.

For example, the data linking system 120 (and/or the entity matchingengine 124) may include a plurality of processors. The data linkingsystem 120 (and/or the entity matching engine 124) can take advantage ofthese processors by dividing data to process into multiple differentbuckets. As an example, the data linking system 120 (and/or the entitymatching engine 124) can group the data to process into 3000 or moredifferent buckets (e.g., by federal information processing standards(FIPS) code, zip code, etc.). The data linking system 120 (and/or theentity matching engine 124) can assign each bucket to a particularprocessor. By grouping data into different buckets, the data linkingsystem 120 (and/or the entity matching engine 124) can balance the loadin different processors, allowing the processors to run in parallel andprocess the data in the respective assigned bucket. In particular, eachprocessor can perform the entire workflow described herein (e.g., theoperations performed by components of the data linking system 120(and/or the entity matching engine 124)) using the data grouped into thebucket assigned to the respective processor.

As described herein, each identifier is unique. The challenge withparallel processing, however, is to avoid having the same uniqueidentifier be assigned to different properties since the identifiercould be generated independently in different processors. To addressthis challenge, each processor generates and assigns a processing indexto each data record in a bucket. Data records grouped into a bucket maybe organized into rows (e.g., where one row corresponds to one datarecord), and the processing index may be dependent on the bucket numberin which the data record is grouped and the row number of the datarecord.

As an illustrative example, data record A may be grouped into bucket#10001 at row number 100. The processor assigned to bucket #10001 canappend the bucket number with the row number (in any order) to generateand assign a processing index to data record A (e.g., 10010001 or10001100 in this case). Similarly, data record B may be grouped intobucket #10002 at row number 100. The processor assigned to bucket #10002can append the bucket number with the row number (in any order) togenerate and assign a processing index to data record B (e.g., 10010002or 10002100 in this case). Each processor may maintain or persist themaximum row number such that when a processor begins processing a newset of data, the first row number for the new data grouped into thebucket assigned to the processor may be a value higher (e.g., 1 higher)than the maximum row number of the previous set of data records thatwere processed by the processor.

After some or all of the processors have generated and assignedprocessing indices to the data records in the different buckets, theprocessors can select unique identifiers to associate with theprocessing indices. For example, the data linking system 120 (and/or theentity matching engine 124) can generate a common pool of uniqueidentifiers. The number of unique identifiers generated may be the sameor higher than the number of data records being processed by theprocessors (e.g., 10 times higher, 100 times higher, etc.). Eachprocessor can then randomly select a unique identifier from the commonpool and append or otherwise combine the selected unique identifier withthe processing index of a data record to form a unique identifierassociated with the data record. Once a processor selects a uniqueidentifier from the common pool, no other processor may be able toselect that unique identifier. When the processors begin processing anew set of data records, the processors can use the same common pool ofunique identifiers, selecting unique identifiers that remain in thecommon pool. This process can be repeated any number of times for anynumber of new data records to process. Thus, the data linking system 120(and/or the entity matching engine 124) can use these techniques toensure that the processors do not accidentally produce duplicateidentifiers for different data records and/or properties.

Example Block Diagrams for Linking a Data Record to a Unique Identifier

FIGS. 2A-2C are flow diagrams illustrating the operations performed bythe components of the operating environment 100 of FIG. 1 to process adata record to begin the linking or matching process. As illustrated inFIG. 2A, the data analyzer 121 obtains data records from the third partydata store 130 at (1). The data analyzer 121 can obtain the data recordsusing a real-time load (e.g., one data record is obtained at a time, butwith a very fast speed, such as 1 million data records per second)and/or a batch load process (e.g., multiple data records are obtained atthe same time). The data analyzer 121 can obtain the data records in oneof many different data formats, such as AVRO, PARQUET, TXT, ORC, etc.The data records may be included in messages obtained by the dataanalyzer 121, and such messages can be in one of many different messageformats, such as JSON, XML, etc. The data analyzer 121 may thendetermine that a first data record in the data records that are obtainedis not an incremental update data record at (2). Rather, the dataanalyzer 121 determines that the first data record is a standalone datarecord. The data analyzer 121 transmits the first data record to thedata deduper 122 at (3).

The data deduper 122 dedupes the first data record at (4). For example,the data deduper 122 may determine that several data records in the onesobtained from the third party data store 130, including the first datarecord, correspond to the same property. Thus, the data deduper 122 maydiscard the other data records corresponding to the property, retainingthe first data record. By performing the dedupe operation, the datadeduper 122 reduces the amount of processing eventually performed by thedata linking system 120 because repeated processing steps that yieldthat same result (e.g., a determination that these data recordscorrespond to the same unique identifier) can be avoided. The datadeduper 122 can then transmit the first data record to the MVR checker123 at (5).

The MVR checker 123 can analyze the property attribute values includedin the first data record to determine whether there is a minimum amountof information available to potentially identify a matching property(e.g., a matching unique identifier). Here, the MVR checker 123determines that the first data record qualifies as a minimum viablerecord at (6). The MVR checker 123 then transmits the first data recordto the entity matching engine 124 at (7).

As illustrated in FIG. 2B, the MVR checker 123 analyzes the propertyattribute values included in the first data record and determines thatthe first data record does not qualify as a minimum viable record at(6). In response, the MVR checker 123 stores the first data record inthe pending file data store 128 at (7) rather than transmitting thefirst data record to the entity matching engine 124. The first datarecord may then be updated at a later time, allowing the data record tothen potentially be matched by the entity matching engine 124, asdescribed with respect to FIG. 2C.

As illustrated in FIG. 2C, the data analyzer 121 obtains data recordsfrom the third party data store 130 at (1) and determines that a seconddata record that is obtained is an incremental update data record at(2). In particular, the data analyzer 121 determines that the seconddata record is an update of the first data record (e.g., based onmetadata or other data associated with the second data record). Thus,the data analyzer 121 updates the first data record stored in thepending file data store 128 using the second data record at (3). Thedata analyzer 121 then transmits the updated first data record to thedata deduper 122 at (4).

The data deduper 122 dedupes the updated first data record at (5). Forexample, the data deduper 122 may determine that several data records inthe ones obtained from the third party data store 130 and the updatedfirst data record correspond to the same property. Thus, the datadeduper 122 may discard the other data records corresponding to theproperty, retaining the updated first data record. By performing thededupe operation, the data deduper 122 reduces the amount of processingeventually performed by the data linking system 120 because repeatedprocessing steps that yield that same result (e.g., a determination thatthese data records correspond to the same unique identifier) can beavoided. The data deduper 122 can then transmit the updated first datarecord to the MVR checker 123 at (6).

The MVR checker 123 can analyze the property attribute values includedin the updated first data record to determine whether there is a minimumamount of information available to potentially identify a matchingproperty (e.g., a matching unique identifier). Here, the MVR checker 123determines that the updated first data record qualifies as a minimumviable record at (7). The MVR checker 123 then transmits the updatedfirst data record to the entity matching engine 124 at (8).

FIGS. 3A-3B are flow diagrams illustrating the operations performed bythe components of the operating environment 100 of FIG. 1 to link ormatch data records to unique identifiers (e.g., to link or match datarecords to a representation of a specific property). As illustrated inFIG. 3A, the entity matching engine 124 retrieves master record datafrom the master record data store 129 at (1), which can include datafrom some or all of the tables stored in the master record data store129.

The entity matching engine 124 determines that a first data recordcorresponds to an existing unique identifier optionally using artificialintelligence at (2). For example, the entity matching engine 124 uses atiered approach to determine whether some or all of the propertyattribute values in the first data record match property attributevalues stored in the master record data store 129 (e.g., in the masterrecord table) in association with a particular unique identifier. Theentity matching engine 124 may use a machine learning model to identifya potential match if, for example, no match is identified using tiershigher in the hierarchy (e.g., the first through fourth tiers). Becausea match is identified, the entity matching engine 124 transmits thefirst data record to the record updater 125 at (3).

The record updater 125 can link the first data record to the existingunique identifier at (4). For example, the record updater 125 can updatethe master cross-reference table to link the unique identifier with thefirst data record itself or a reference identifier of the first datarecord. Optionally, the record updater 125 can update the master recordhistory table and/or the master record table corresponding to theexisting unique identifier at (5). For example, the record updater 125may update the master record history table if the first data recordincludes old information (e.g., potentially outdated information, wherethe master record table includes property attribute values sourced fromnewer data records), and the record updater 125 may update the masterrecord table if the first data record includes new information thatdiffers from the property attribute values included in the entry in themaster record table corresponding to the existing unique identifier.

As illustrated in FIG. 3B, the entity matching engine 124 retrievesmaster record data from the master record data store 129 at (1), whichcan include data from some or all of the tables stored in the masterrecord data store 129.

The entity matching engine 124 determines that a first data record doesnot correspond to an existing unique identifier optionally usingartificial intelligence at (2). For example, the entity matching engine124 uses a tiered approach to determine whether some or all of theproperty attribute values in the first data record match propertyattribute values stored in the master record data store 129 (e.g., inthe master record table) in association with a particular uniqueidentifier. The entity matching engine 124 may use a machine learningmodel to identify a potential match if, for example, no match isidentified using tiers higher in the hierarchy (e.g., the first throughfourth tiers). Because a match is not identified, the entity matchingengine 124 transmits the first data record to the ID generator 126 at(3).

The ID generator 126 can generate a new unique identifier for the firstdata record at (4). For example, the ID generator 126 can assign theproperty corresponding to the first data record a random number. The IDgenerator 126 can then create a new master record in the master recorddata store 129 at (5). For example, the ID generator 126 can create newentries in some or all of the tables of the master record data store 129that are each associated with the newly generated unique identifier andthat are populated with some or all of the information included in thefirst data record.

Example Block Diagram for Generating a User Interface with Property Data

FIG. 4 is a flow diagram illustrating the operations performed by thecomponents of the operating environment 100 of FIG. 1 to provideproperty data to a user device 102 for display in an interactive userinterface. As illustrated in FIG. 4, the user device 102 requestsinformation for a first property at (1). The user interface generator127 can receive the request, and retrieve data from the master recordtable corresponding to the first property at (2). For example, therequest from the user device 102 may include some property attributevalues. The user interface generator 127 can query the master recorddata store 129 (e.g., the master record table) for an entry that matchessome or all of these property attribute values. The master record datastore 129 can then return some or all of the property attribute valuesfor the entry that includes the matching property attribute values. Theuser interface generator 127 can also identify the unique identifiercorresponding to the entry, and retrieve additional data from otherentries in the other tables stored in the master record data store 129that are associated with the identified unique identifier

The user interface generator 127 can generate user interface data basedon the retrieved data at (3). The user interface generator 127 can thentransmit the user interface data to the user device 102 at (4).

The user device 102 may then render and display a user interfacedepicting the information associated with the first property using theuser interface data at (5). Example user interfaces are described belowwith respect to FIGS. 5A through 6.

Example User Interfaces

FIGS. 5A-5B illustrate an example user interface 500 depicting propertyattribute values for a property requested by a user via a user device102. The user interface 500 may be rendered and displayed on a userdevice 102 based on user interface data generated by the user interfacegenerator 127 of the data linking system 120.

As illustrated in FIG. 5A, the user interface 500 includes a window orframe 510 depicting a geographic map 550. The user interface 500 furtherincludes a window or frame 540 that includes a search field 542 and aslider bar 544. A user may enter information describing a property inthe search field 542. For example, the property information can be anyproperty attribute value. In response to the user entering the propertyinformation, the user device 102 may transmit the information to thedata linking system 120 (e.g., the user interface generator 127). Theuser interface generator 127 may then use the transmitted propertyinformation to query the master record data store 129 and identify theproperty corresponding to the provided property attribute values, andsubsequently identify additional property attribute values and/or otherinformation (e.g., information stored in the various tables of themaster record data store 129, such as record history, event history,etc.) of that property in a manner as described above. The userinterface generator 127 can then forward these identified propertyattribute values in the form of user interface data to the user device102, which can update the user interface 500 to display the identifiedproperty attribute values, as shown in box 554.

For example, based on the identified property attribute values, thegeographic map 550 may be updated to include an indication of ageographic location of the property (represented by box 552) as well asa visualization of the property attribute values (as listed in box 554).FIG. 5A illustrate a box 554 as a pop-up or tooltip window showing theproperty attribute values, but the property attribute values can bedisplayed in any other type of manner.

As described above, a property can be split, merged, become inactive,etc. The master record data store 129 (e.g., the master event historytable) may have a record of such events, with the unique identifier ofthe original property linked to the unique identifier of the splitproperty, the unique identifier of the original property linked to theunique identifier of the merged property, etc. Thus, the user interfacedata generated by the user interface generator 127 may include thisinformation, including the property attribute values of the propertywith a unique identifier linked to the unique identifier of the subjectproperty. Accordingly, a user can move the slider 544 to view how theproperty has changed over time, if at all.

For example, as illustrated in FIG. 5B, a user has moved the slider 544from a position corresponding to Jul. 1, 2018 to a positioncorresponding to Jul. 1, 2008. In response, the user interface 500 isupdated to depict a new box 556 in the map 550 that represents thelocation and size of the property searched for by the user 10 yearsprior. As shown in FIG. 5B, the property searched for by the user (e.g.,1234 Main St.) was larger in 2008 and had a different address (e.g.,1000 Main St.). The property attribute values of the larger property arealso different (as shown in box 558), with the box 558 indicating thatthe property was subdivided in 2009 to form the property searched for bythe user as well as potentially other properties.

A user interface generally has a finite amount of space. However, theuser interface 500 is structured with pop-up windows (e.g., boxes 554and 558), a slider 544, and/or other features that dynamically show theuser information about a property and information about relatedproperties (e.g., a parent property, a child property, etc.) despitethis finite amount of space.

In addition, the user interface 500 is structured in a manner to reducethe number of navigational steps that a user may need to perform to viewdesired property information. For example, with the user interface 500the user only has to enter a single search query to view informationabout multiple properties. In prior user interfaces, a user may have toenter a different search query for each desired property or navigatethrough different windows or pages to view the same information as isavailable in the user interface 500, even if the properties have aparent or child relationship.

While the user interface 500 is illustrated as having a slider 544, thisis not meant to be limiting. The user interface 500 could insteadinclude a dropdown box, a menu, or other like user interface features toallow a user to switch between viewing the searched-for property and arelated property.

FIG. 6 illustrates another example user interface 600 depicting propertyattribute values for a property requested by a user via a user device102. The user interface 600 may be rendered and displayed on a userdevice 102 based on user interface data generated by the user interfacegenerator 127 of the data linking system 120.

As illustrated in FIG. 6, the user interface 600 includes a window orframe 610 depicting property attribute values for a property searchedfor by a user using search field 612. The user interface 600 furtherincludes a geographic map 640 depicting a location of the property (asrepresented by box 642), a property history graph 650, and/or a propertyhistory table 660. The property history graph 650 and/or the propertyhistory table 660 may be other ways in which the user interface 600 candisplay information related to a searched-for property and anyproperties related to the searched-for property given the finite amountof space available in a user interface and in a manner that can reducethe number of navigational steps a user has to perform to view desiredproperty information.

As shown in the property history graph 650, a property referenced byunique identifier 1478500416 may have been subdivided into twoproperties on Nov. 3, 2009: a property with unique identifier 4530591645and a property with unique identifier 6455010909. The property historygraph 650 may further highlight or otherwise identify the property inthe graph that the user searched for (e.g., here, the user searched fora property with the unique identifier 4530591645).

The property history table 660 may include additional information, suchas the dates that a property was active (e.g., before it was subdivided,merged, destroyed, etc.) and the approximate geographic coordinates (orspatial boundaries) of the property.

Example Entity Matching Engine Service

FIG. 7 is a more detailed block diagram of the entity matching engine124. As described herein, the entity matching engine 124 can performpre-processing operations, which are described herein with respect toFIG. 7. In addition, the entity matching engine 124 can operate as astandalone, network-accessible service. For example, a user device 102can send a request to the entity matching engine 124 to identify a datarecord that matches a data record included in the request. Thus, theentity matching engine 124, as a service, can be used by user devices102 to identify matches for property data records, financial datarecords, vehicle data records, and/or any other types of data records.

The entity matching engine 124 may include various modules, components,data stores, and/or the like to provide the data matching functionalitydescribed herein. For example, the entity matching engine 124 mayinclude an entity classifier 721, an entity affiliator 722, a contextassociator 723, a data cleanser 724, a data standardizer 725, acontext-based matcher 726, and a user interface generator 727.

The entity classifier 721 may receive a data record (e.g., from the MVRchecker 123, from a user device 102, etc.) and use artificialintelligence to identify one or more entities in the data record. Anentity can be a person, a house, and/or the like. For example, theentity classifier 721 may train a machine learning model to identifyentities based on a training dataset (e.g., data records in whichentities are labeled) or generate a database that includes a mappingidentifying different types of values that may be present in a datarecord and whether such values represent entities. The entity classifier721 can then apply a received data record as an input to the trainedmachine learning model, which produces a list of probable entities, orcan query the database using property attribute values included in thedata record to identify possible entities. The machine learning modelcan be continuously or periodically updated or retrained (e.g., by theentity classifier 721) based on user feedback that is converted intotraining data with labels to identify situations in which entities werecorrectly and incorrectly identified. The use of user feedback to updateor retrain the machine learning model may be gradually reduced as themachine learning model becomes more accurate.

The entity affiliator 722 can then identify a relationship between theidentified entities. In the context of real property, a relationshipbetween two entities could be, for example, that a first entity (e.g., aperson) is buying (or selling, renting, etc., which is the relationship)a second entity (e.g., a house). For example, the entity affiliator 722may train a machine learning model to identify a relationship betweenentities based on a training dataset (e.g., data records in whichentities and the relationships of such entities are labeled) or generatea database that includes a mapping identifying different types ofentities that may be present in data record and possible relationshipsof such entities. The entity affiliator 722 can then apply theidentified entities and a received data record as an input to thetrained machine learning model, which produces a list of probablerelationships, or can query the database using property attribute valuesincluded in the data record and/or the identified entities to identifypossible relationships. The machine learning model can be continuouslyor periodically updated or retrained (e.g., by the entity affiliator722) based on user feedback that is converted into training data withlabels to identify situations in which relationships were correctly andincorrectly identified. The use of user feedback to update or retrainthe machine learning model may be gradually reduced as the machinelearning model becomes more accurate.

The context associator 723 can identify a context of the received datarecord, given the entities and the relationship between the entities. Inthe context of real property, a context may be that a first entity(e.g., a person) gets a mortgage (e.g., the context) to buy a secondentity (e.g., a house). For example, the context associator 723 cantrain a machine learning model using supervised and/or unsupervisedtechniques. In the supervised example, the context associator 723 cantrain the machine learning model using training data that includes datarecords in which entities, relationships between entities, and thecontext of such relationships are labeled. In the unsupervised example,the machine learning model initially trained by the context associator723 can use graphing techniques to infer relationships between entitiesand/or contexts. The context associator 723 can apply the data record,the identified entities, and/or the identified relationships as an inputto the trained machine learning model to determine probable contexts.The machine learning model can be continuously or periodically updatedor retrained (e.g., by the context associator 723) based on userfeedback that is converted into training data with labels to identifysituations in which contexts were correctly and incorrectly identified.The use of user feedback to update or retrain the machine learning modelmay be gradually reduced as the machine learning model becomes moreaccurate.

Alternatively or in addition, the context associator 723 can identify acontext of the received data record based on the property attributevalues included therein. For example, the context associator 723 mayhave generated a database with mappings between property attributevalues and corresponding contexts. As an illustrative example, a datarecord with a loan amount, a monthly payment, etc. may be mapped to amortgage context. The context associator 723 can query the databaseusing the property attribute values included in the received data recordto obtain a probable context.

The entity classifier 721, the entity affiliator 722, and the contextassociator 723 may not modify the data record, but these components mayinform the data cleanser 724 and/or the data standardizer 725 how tomodify the data record, if appropriate, and may inform the context-basedmatcher 726 how to perform a match. For example, the data cleanser 724can, using a set of rules, remove excess characters from the data record(e.g., extra whitespaces, periods, commas, colons, etc.).

The data cleanser 724 can also use a set of rules to modify certainproperty attribute values. For example, the rules may identify a rangeof acceptable values for certain property attributes. As an illustrativeexample, the rules may identify that the number of bedrooms in aproperty can be between 0 and 30. If a data record indicates that theproperty has 100 bedrooms, the data cleanser 724 can modify thisproperty attribute value to the highest allowable value (e.g., 30) (orthe lowest allowable value if the original value falls below theacceptable range). Alternatively, the data cleanser 724 can simply erasethe original value, leaving the value empty. The rules implemented bythe data cleanser 724 may be chosen by the data cleanser based on theidentified entities, relationships, and/or context.

The data standardizer 725 can standardize the data record. For example,the data standardizer 725 can rename column names to match a standardname, reformat abbreviations or codes into a standard format (e.g.,change “BDR” to “BDRMS”), standardize legal names (e.g., include periodsafter each middle initial such that a data record with “John H Smith”becomes “John H. Smith”), standardize addresses (e.g., use 9 digit zipcodes instead of 5 digit zip codes), and/or the like.

The data cleanser 724 and the data standardizer 725 implementpre-processing operations to place the data record in a better conditionfor obtaining a match, thereby improving the entity matching engine 124match rate. Thus, the data cleanser 724 and the data standardizer 725can improve the match rate, and therefore reduce further processingimplemented by other systems that obtain data from the entity matchingengine 124 and/or the data linking system 120.

Once the data record is cleansed and standardized, the data standardizer725 can send the potentially modified data record to the context-basedmatcher 726. The context-based matcher 726 may implement the tieredapproach discussed above, using information from the master record datastore 129, to identify a possible match. The context-based matcher 726can output a matching data record or unique identifier, transmittingthis information to the user device 102 and/or the user interfacegenerator 727.

The user interface generator 727 can generate user interface data that,when rendered by a user device 102, causes the user device to display auser interface depicting information identifying the match (if a matchoccurs) or indicating that no match occurred. The user interface may besimilar to the user interfaces 500 and 600 discussed above.

Example Block Diagrams for Preparing a Data Record to be Matched

FIGS. 8A-8B are flow diagrams illustrating the operations performed bythe components of the entity matching engine 124 of FIGS. 1 and 7 toprepare a data record to be matched and to perform the matching. Asillustrated in FIG. 8A, the entity classifier 721 receives a data recordtransmitted by a user device 102 (e.g., via the network 110) or by theMVR checker 123 at (1). In response, the entity classifier 721identifies entities in the data record at (2). For example, the entityclassifier 721 can use a trained machine learning model to identifypossible entities. The entity classifier 721 then transmits the datarecord and the identified entities to the entity affiliator 722 at (3).

The entity affiliator 722 identifies a relationship of the identifiedentities. For example, the entity affiliator 722 can use a trainedmachine learning model, providing the identified entities as an input,to identify possible relationship(s). The entity affiliator 722 can thentransmit the data record, the identified entities, and/or the identifiedrelationships to the context associator 723 at (5).

The context associator 723 can identify a context of the data recordbased on property attribute values included in the data record and/orbased on artificial intelligence at (6). For example, the contextassociator 723 can use a trained machine learning model, providing theidentified entities, the identified relationships, and/or the propertyattribute values as inputs, to identify a possible context.Alternatively or in addition, the context associator 723 can query adatabase in which entities and/or relationships are mapped to contextsto identify a possible context. Once identified, the context associator723 can transmit the data record, identified entities, identifiedrelationships, and/or identified context to the data cleanser 724 at(7).

The data cleanser 724 can cleanse the data record. For example, the datacleanser 724 can use the identified context to identify a set of rulesthat are used to modify the data record to remove outliers (e.g.,property attribute values that fall outside an acceptable range ofvalues) and/or to remove extra characters (e.g., extra whitespaces,extra punctuation, etc.) at (8). The data cleanser 724 may not alwaysmodify the data record (e.g., if the data record does not include anyoutliers or extra characters), but the data cleanser 724 is illustratedas modifying the data record in FIG. 8A for illustrative purposes. Oncethe data record is analyzed and modified, the data cleanser 724transmits the modified data record to the data standardizer 725 at (9).

The data standardizer 725 standardizes data in the modified data recordat (10). For example, the data standardizer 725 can rename columnheaders, convert abbreviations or codes into a common format,standardize names, standardize physical street addresses, and/or thelike. The data standardizer 725 may not always modify the data record(e.g., if the data record conforms to a standard format), but the datastandardizer 725 is illustrated as modifying the data record in FIG. 8Afor illustrative purposes. Once the data record is analyzed andmodified, the data standardizer 725 transmits the standardized datarecord to the context-based matcher 726 at (11).

As illustrated in FIG. 8B, the context-based matcher 726 can attempt toidentify a match to the standardized data record and provide anindication of whether a match occurred or did not occur to the userdevice 102 or to a component of the data linking system 120. Forexample, the context-based matcher 726 can retrieve master record datafrom the master record data store 129 at (1). For example, the masterrecord data can be data from any of the tables stored in the masterrecord data store 129.

The context-based matcher 726 can then compare the standardized datarecord with the master record data using a tiered analysis at (2). Forexample, the context-based matcher 726 can compare the propertyattribute values in the standardized data record with property attributevalues present in the master record according to the rules in a firsttier. If no match is identified (e.g., the property attribute values inthe standardized data record analyzed in the first tier do not match anyentry in the master record table, for example), then the context-basedmatcher 726 attempts to identify a match according to the rules in asecond tier, and so on. If the first four tiers do not result in amatch, then the context-based matcher 726 analyzes the standardized datarecord using artificial intelligence to attempt to identify a match at(3). If using artificial intelligence yields at least one match, thecontext-based matcher 726 may further analyze the match(es) in anattempt to remove false positives generated by the artificialintelligence. As an example, a match output by the context-based matcher726 can be a unique identifier, another data record, a propertyattribute value of a property, and/or the like.

Optionally, if the user device 102 is the device that originallyprovided the data record for matching purposes, the context-basedmatcher 726 transmits an indication of a match or no match to the userinterface generator 727 at (4). The user interface generator 727 canthen generate user interface data at (5) that, when rendered, causes theuser device 102 to display an interactive user interface in which a usercan view the match (or an indication that no match occurred) andpotentially related information (e.g., information identifying a parentor child property). The user interface generator 727 can then transmitthe user interface data to the user device 102 at (6).

Optionally, if the data linking system 120 uses the entity matchingengine 124 to identify a match to a data record obtained from a thirdparty data store 130, the context based-matcher 726 transmits at (7) thestandardized data record and/or an identified unique identifier to therecord updater 125 if there is a match, or the standardized data recordto the ID generator 126 if there is no match.

Example Data Linking Routine

FIG. 9 is a flow diagram depicting a data linking routine 900illustratively implemented by a data linking system, according to oneembodiment. As an example, the data linking system 120 of FIG. 1 can beconfigured to execute the data linking routine 900. The data linkingroutine 900 begins at block 902.

At block 904, data records are obtained. For example, the data recordscan be obtained from one or more third party data stores 130.

At block 906, a determination is made that a first data record in theobtained data records is not an incremental update. For example, thefirst data record may be an incremental update to another data record ifthe first data record included a label, annotation, or other metadatalinking the data record to a previous data record.

At block 908, data records duplicative of the first data record areremoved. For example, data records in the obtained data records may beduplicative of the first data record if these data records allcorrespond to the same property and/or include the same propertyattribute values. By removing the duplicative data records, the datalinking system 120 can minimize the number of operations that areperformed that yield the same result (e.g., a match to the same uniqueidentifier).

At block 910, a determination is made that the first data recordqualifies as a minimum viable record. For example, the first data recordmay qualify as a minimum viable record if the first data record includesa minimum number of property attribute values.

At block 912, a determination is made that the first data recordcorresponds to an entry in a master record using artificialintelligence. For example, the data linking system 120 may implement atiered approach or analysis to evaluate whether the first data recordmatches any entry in the master record table (e.g., corresponds to thesame property as an entry in the master record table). The first fourtiers of the tiered approach may not yield a match, but a trainedmachine learning model, which receives the first data record and some orall of the data in the master record data store 129 as an input, mayidentify one or more possible matches. The identified match(es) may berepresented as unique identifiers.

At block 914, the master record is updated using the first data record.For example, the data linking system 120 may determine that the firstdata record includes property attribute values that are newer than thoseincluded in the master record table. Thus, the master record table canbe updated to include the newer property attribute values of the firstdata record.

At block 916, user interface data is generated and transmitted. Forexample, the user interface data can be generated using some or all ofthe data in the master record data store 129 corresponding to the uniqueidentifier identified as a match. A user device 102 can then use theuser interface data to render and display an interactive user interfacethat allows a user to view property attribute values for a searched-forproperty and/or any properties related to the searched-for property.After the user interface data is generated and transmitted, the datalinking routine 900 ends, as shown at block 918.

Example Data Matching Routine

FIG. 10 is a flow diagram depicting a data matching routine 1000illustratively implemented by an entity matching engine, according toone embodiment. As an example, the entity matching engine 124 of FIG. 1can be configured to execute the data matching routine 1000. The datamatching routine 1000 begins at block 1002.

At block 1004, a data record is obtained. For example, the data recordcan be obtained from a user device 102 or another component of the datalinking system 120 (e.g., the MVR checker 123).

At block 1006, entities in the data record are identified. For example,a trained machine learning model can be used to identify the entities inthe data record.

At block 1008, a relationship of the identified entities is identified.For example, another trained machine learning model can be used toidentify the relationship given the identified entities and/or the datarecord as an input to the model.

At block 1010, a context of the data record is identified based onproperty attribute values included in the data record and/or artificialintelligence. For example, a database may be available that includes amapping between entities, certain relationships, and/or correspondingcontexts. As another example, a machine learning model may be trained,using supervised or unsupervised techniques, to identify contexts givena data record, entities, and/or relationships.

At block 1012, a data record is modified to cleanse and standardize thedata included therein. For example, outlier property attribute valuescan be modified or removed altogether, extra characters can be removed,columns can be renamed, abbreviations or codes can be translated into acommon format, names can be standardized, physical street addresses canbe standardized, and/or the like. Modifying the data record prior toattempting to identify a match may increase the match rate, andultimately improve the computing performance of the entity matchingengine 124, the data linking system 120, a user device 102, and/or anexternal system (not shown).

At block 1014, the modified data record is compared against masterrecord data optionally using artificial intelligence. For example,artificial intelligence may be used to perform the comparison andattempt to identify a match between the modified data record and anotherdata record and/or a unique identifier if other comparisons in a tieredanalysis fail to yield a match.

At block 1016, a determination is made as to whether the modified datarecord has a match based on the comparison. Once the determination ismade, the entity matching engine 124 can forward the results to the userdevice 102, the user interface generator 727, the record updater 125,and/or the ID generator 126. After the determination is made, the datamatching routine 1000 ends, as shown at block 1018.

Additional Embodiments

Various example user devices 102 are shown in FIG. 1, including adesktop computer, laptop, and a mobile phone, each provided by way ofillustration. In general, the user devices 102 can be any computingdevice such as a desktop, laptop or tablet computer, personal computer,wearable computer, server, personal digital assistant (PDA), hybridPDA/mobile phone, mobile phone, electronic book reader, set-top box,voice command device, camera, digital media player, and the like. A userdevice 102 may execute an application (e.g., a browser, a stand-aloneapplication, etc.) that allows a user to search for information on aproperty and view the results, and/or to provide a data record and viewunique identifiers and/or other data records that potentially match theprovided data record.

The network 110 may include any wired network, wireless network, orcombination thereof. For example, the network 110 may be a personal areanetwork, local area network, wide area network, over-the-air broadcastnetwork (e.g., for radio or television), cable network, satellitenetwork, cellular telephone network, or combination thereof. As afurther example, the network 110 may be a publicly accessible network oflinked networks, possibly operated by various distinct parties, such asthe Internet. In some embodiments, the network 110 may be a private orsemi-private network, such as a corporate or university intranet. Thenetwork 110 may include one or more wireless networks, such as a GlobalSystem for Mobile Communications (GSM) network, a Code Division MultipleAccess (CDMA) network, a Long Term Evolution (LTE) network, or any othertype of wireless network. The network 110 can use protocols andcomponents for communicating via the Internet or any of the otheraforementioned types of networks. For example, the protocols used by thenetwork 110 may include Hypertext Transfer Protocol (HTTP), HTTP Secure(HTTPS), Message Queue Telemetry Transport (MQTT), ConstrainedApplication Protocol (CoAP), and the like. Protocols and components forcommunicating via the Internet or any of the other aforementioned typesof communication networks are well known to those skilled in the artand, thus, are not described in more detail herein.

The machine learning models disclosed herein can implement any one ofthe following algorithms: a neural network algorithm, a Support VectorMachine algorithm, a Probabilistic Graphic Model algorithm, a DecisionTree model algorithm, an extreme gradient boost (XGboost), a generalizedlogistic regression, canopy clustering, active learning, random forest,and/or the like. Such algorithms cannot be performed by a human due tothe fact that machine learning involves the iterative learning fromdata. A computing device that uses a machine learning algorithm toperform an action is not programmed by a human with explicitinstructions that cause the action to be performed. Rather, a computingdevice that uses a machine learning process to perform an action makesdecisions or predictions in the course of performing the action based ona model learned or trained using sample data. Thus, there is not a knownset of instructions that a human could simply follow to mimic theactions performed using a machine learning process.

Terminology

All of the methods and tasks described herein may be performed and fullyautomated by a computer system. The computer system may, in some cases,include multiple distinct computers or computing devices (e.g., physicalservers, workstations, storage arrays, cloud computing resources, etc.)that communicate and interoperate over a network to perform thedescribed functions. Each such computing device typically includes aprocessor (or multiple processors) that executes program instructions ormodules stored in a memory or other non-transitory computer-readablestorage medium or device (e.g., solid state storage devices, diskdrives, etc.). The various functions disclosed herein may be embodied insuch program instructions, or may be implemented in application-specificcircuitry (e.g., ASICs or FPGAs) of the computer system. Where thecomputer system includes multiple computing devices, these devices may,but need not, be co-located. The results of the disclosed methods andtasks may be persistently stored by transforming physical storagedevices, such as solid state memory chips or magnetic disks, into adifferent state. In some embodiments, the computer system may be acloud-based computing system whose processing resources are shared bymultiple distinct business entities or other users.

Depending on the embodiment, certain acts, events, or functions of anyof the processes or algorithms described herein can be performed in adifferent sequence, can be added, merged, or left out altogether (e.g.,not all described operations or events are necessary for the practice ofthe algorithm). Moreover, in certain embodiments, operations or eventscan be performed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors or processor cores or onother parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware (e.g., ASICs or FPGAdevices), computer software that runs on computer hardware, orcombinations of both. Moreover, the various illustrative logical blocksand modules described in connection with the embodiments disclosedherein can be implemented or performed by a machine, such as a processordevice, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A processor device can be amicroprocessor, but in the alternative, the processor device can be acontroller, microcontroller, or logic circuitry that implements a statemachine, combinations of the same, or the like. A processor device caninclude electrical circuitry configured to process computer-executableinstructions. In another embodiment, a processor device includes an FPGAor other programmable device that performs logic operations withoutprocessing computer-executable instructions. A processor device can alsobe implemented as a combination of computing devices, e.g., acombination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Although described hereinprimarily with respect to digital technology, a processor device mayalso include primarily analog components. For example, some or all ofthe rendering techniques described herein may be implemented in analogcircuitry or mixed analog and digital circuitry. A computing environmentcan include any type of computer system, including, but not limited to,a computer system based on a microprocessor, a mainframe computer, adigital signal processor, a portable computing device, a devicecontroller, or a computational engine within an appliance, to name afew.

The elements of a method, process, routine, or algorithm described inconnection with the embodiments disclosed herein can be embodieddirectly in hardware, in a software module executed by one or moreprocessor devices, or in a combination of the two. A software module canreside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROMmemory, registers, hard disk, a removable disk, a CD-ROM, or any otherform of a non-transitory computer-readable storage medium. An exemplarystorage medium can be coupled to the processor device such that theprocessor device can read information from, and write information to,the storage medium. In the alternative, the storage medium can beintegral to the processor device. The processor device and the storagemedium can reside in an ASIC. The ASIC can reside in a user terminal. Inthe alternative, the processor device and the storage medium can resideas discrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements or steps.Thus, such conditional language is not generally intended to imply thatfeatures, elements or steps are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without other input or prompting, whether thesefeatures, elements or steps are included or are to be performed in anyparticular embodiment. The terms “comprising,” “including,” “having,”and the like are synonymous and are used inclusively, in an open-endedfashion, and do not exclude additional elements, features, acts,operations, and so forth. Also, the term “or” is used in its inclusivesense (and not in its exclusive sense) so that when used, for example,to connect a list of elements, the term “or” means one, some, or all ofthe elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, or Z). Thus,such disjunctive language is not generally intended to, and should not,imply that certain embodiments require at least one of X, at least oneof Y, and at least one of Z to each be present.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it can beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As can berecognized, certain embodiments described herein can be embodied withina form that does not provide all of the features and benefits set forthherein, as some features can be used or practiced separately fromothers. The scope of certain embodiments disclosed herein is indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A system for matching data records, the systemcomprising: a master record data store configured to store dataassociated with a plurality of properties; and a computing systemcomprising one or more computing devices, wherein the computing systemis configured to communicate with the master record data store and isconfigured with specific computer-executable instructions to at least:train a first machine learning model with one or more feature-basedrepresentations each associated with a data record pair and labeled withan indication of whether the respective data record pair is a match;process a request received from a user device via a network, wherein therequest specifies a data record; identify a first entity and a secondentity in the data record; identify a relationship between the firstentity and the second entity; identify a context associated with thefirst entity, the second entity, and the identified relationship using atrained machine learning model trained with one or more data recordslabeled to identify corresponding contexts; cleanse and standardize datain the data record using one or more rules selected based on theidentified context to form a modified data record; determine that afirst property in the plurality of properties shares a first propertyattribute value with the modified data record; generate a firstfeature-based representation of a combination of property attributevalues of the modified data record and property attribute values of thefirst property; determine that the modified data record and the firstproperty have a match probability above a threshold value based on anapplication of the first feature-based representation as an input to thetrained first machine learning model; and output an indication of thedetermination such that the user device receives an indication that thedata record matches the first property.
 2. The system of claim 1,wherein the indication received by the user device is a user interface,and wherein the user interface depicts property attribute values of thefirst property.
 3. The system of claim 2, wherein the first property isa child property of a second property in the plurality of properties,and wherein the user interface further depicts a slider that, when movedfrom a date after creation of the first property to a date before thecreation of the first property, causes the user interface to update todepict property attribute values of the second property.
 4. The systemof claim 1, wherein the computing system is further configured withspecific computer-executable instructions to at least: compare a secondproperty attribute value in the data record with a range of acceptablevalues; determine that the second property attribute value is outsidethe range; and remove the second property attribute value from the datarecord.
 5. The system of claim 1, wherein the computing system isfurther configured with specific computer-executable instructions to atleast remove extra characters from the data record.
 6. The system ofclaim 1, wherein the computing system is further configured withspecific computer-executable instructions to at least: identify a firstentity and a second entity in the data record using a second trainedmachine learning model; and identify a relationship between the firstentity and the second entity based on an application of the first entityand the second entity as inputs to a third trained machine learningmodel.
 7. The system of claim 1, wherein the one or more data recordslabeled to identify corresponding contexts comprises data records inwhich entities, relationships between the entities, and the context ofthe relationships are labeled.
 8. The system of claim 1, wherein thecomputing system is further configured with specific computer-executableinstructions to at least: determine that a second property in theplurality of properties shares the first property attribute value withthe modified data record; generate a second feature-based representationof a combination of property attribute values of the modified datarecord and property attribute values of the second property; anddetermine that the modified data record and the second property have amatch probability below the threshold value based on an application ofthe second feature-based representation as an input to the first trainedmachine learning model.
 9. A computer-implemented method of matchingdata records, the method comprising: as implemented by one or morecomputing devices configured with specific computer-executableinstructions, training a first machine learning model with one or morefeature-based representations each associated with a data record pairand labeled with an indication of whether the respective data recordpair is a match; obtaining a data record; identifying a contextassociated with the data record using a trained machine learning modeltrained with one or more data records labeled to identify correspondingcontexts; cleansing data in the data record using one or more rulesselected based on the identified context to form a modified data record;standardizing data in the modified data record to form a second modifieddata record; determining that a first property in the plurality ofproperties shares a first property attribute value with the modifieddata record; generating a first feature-based representation of acombination of property attribute values of the modified data record andproperty attribute values of the first property; determining that thesecond modified data record and the first property have a matchprobability above a threshold value based on an application of the firstfeature-based representation as an input to the trained first machinelearning model; and causing output of an indication that the data recordmatches the first property.
 10. The computer-implemented method of claim9, wherein the indication is a user interface, and wherein the userinterface depicts property attribute values of the first property. 11.The computer-implemented method of claim 10, wherein the first propertyis a child property of a second property, and wherein the user interfacefurther depicts a slider that, when moved from a date after creation ofthe first property to a date before the creation of the first property,causes the user interface to update to depict property attribute valuesof the second property.
 12. The computer-implemented method of claim 9,wherein cleansing data further comprises: comparing a second propertyattribute value in the data record with a range of acceptable values;determining that the second property attribute value is outside therange; and removing the second property attribute value from the datarecord.
 13. The computer-implemented method of claim 9, whereincleansing data further comprises removing extra characters from the datarecord.
 14. The computer-implemented method of claim 9, furthercomprising: determining that a second property shares the first propertyattribute value with the second modified data record; generating asecond feature-based representation of a combination of propertyattribute values of the second modified data record and propertyattribute values of the second property; and determining that the secondmodified data record and the second property have a match probabilitybelow the threshold value based on an application of the secondfeature-based representation as an input to the first trained machinelearning model.
 15. Non-transitory, computer-readable storage mediacomprising computer-executable instructions for matching data records,wherein the computer-executable instructions, when executed by acomputer system, cause the computer system to: train a first machinelearning model with one or more feature-based representations eachassociated with a data record pair and labeled with an indication ofwhether the respective data record pair is a match; obtain a datarecord; identify a context associated with the data record using atrained machine learning model trained with one or more data recordslabeled to identify corresponding contexts; modify the data record usingone or more rules selected based on the identified context to improve amatch rate; determine that a first property in the plurality ofproperties shares a first property attribute value with the modifieddata record; generate a first feature-based representation of acombination of property attribute values of the modified data record andproperty attribute values of the first property; determine that themodified data record and the first property have a match probabilityabove a threshold value based on an application of the firstfeature-based representation as an input to the trained first machinelearning model; and generate an output that causes generation of arepresentation indicating that the data record matches the firstproperty.
 16. The non-transitory, computer-readable storage media ofclaim 15, wherein the representation is a user interface, and whereinthe user interface depicts property attribute values of the firstproperty.
 17. The non-transitory, computer-readable storage media ofclaim 16, wherein the first property is a child property of a secondproperty, and wherein the user interface further depicts a slider that,when moved from a date after creation of the first property to a datebefore the creation of the first property, causes the user interface toupdate to depict property attribute values of the second property. 18.The non-transitory, computer-readable storage media of claim 15, whereinthe computer-executable instructions further cause the computer systemto: determine that a second property shares the first property attributevalue with the modified data record; generate a second feature-basedrepresentation of a combination of property attribute values of themodified data record and property attribute values of the secondproperty; and determine that the modified data record and the secondproperty have a match probability below the threshold value based on anapplication of the second feature-based representation as an input tothe first trained machine learning model.